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Microgrids are small scale power systems with local resources for generation; consumption and storage, that can operate connected to the main grid or islanded. For the islanding operation of microgrids, two important tasks are to share the load demand and maintain the voltage and frequency stabilities. In order to achieve this goal, a hierarchical control structure can be employed. This research presents a solution technique for finding the optimal site, production and droop coefficients of distributed generation (DG) units in microgrids. In this paper, three main factors are scrutinized through a multi-objective optimization approach. These factors include fuel consumption cost, stability and variations of voltage. To solve this optimization problem, an Imperialist Competitive Algorithm-Genetic Algorithm (ICA-GA) is presented. A fuzzy approach is used to search in non-dominated outcomes and to find the best answer. To show the effectiveness of the proposed method, it is implemented on 33-buses IEEE test systems. The simulation results exhibit the ability and efficiency of the proposed scheme to find the optimal solutions.

Summary Nowadays, the incorporation of wind power in electrical grids and electricity markets is grown. Due to the fluctuation of wind speed, one of the main challenges of wind power would be selling power directly to the wholesale markets. A method for solving this challenge is coordination of wind power with energy storages, cascaded hydro, or gas turbine units in bidding strategy and operation. By coordinating with gas turbine units, wind power can be incorporated in real‐time markets with fewer capital costs. In this paper, a stochastic bi‐level optimization is proposed for coordinated wind power and gas turbine units in the real‐time market. The uncertainties of wind power, demands, rivals' biddings, and limitations of natural gas are considered. The optimal bidding strategy is determined by adding a barrier term into the objective function of the proposed optimization model. The added term is based on the concept of conditional value at risk.

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